Segmentation of given point clouds: Segmentation of point cloud data is one of the popular 3D tasks. We carried out this experiment on the ShapeNet benchmark [1]} and followed the data split in [2]}. ShapeNet contains 16881 models of 16 categories and they are labelled in 50 parts in total. Like [1]}, the Intersection-over-Union (IoU) of a shape is computed by averaging the IoUs of different parts in that shape, and the IoU of each category is obtained by averaging the IoUs of all the shapes belonging to that category. The results are summarized in Table REF . When the training set is small, the performance of our network is on par with the other methods. Our technique extracts common deep features between shapes so it needs a sufficiently large number of training samples. From the table, it can be seen that Pattern-Net outperforms the existing methods on relatively large categories like airplane, car, chair and lamp. A sample result of each category is illustrated in Fig. REF .
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